We have investigated the accuracy of the templating of digital radiographs in planning total hip replacement using two common object-based
Digital radiography is becoming widespread. Accurate pre-operative templating of digital images of the hip traditionally involves positioning a
To develop and validate patient-centred algorithms that estimate individual risk of death over the first year after elective joint arthroplasty surgery for osteoarthritis. A total of 763,213 hip and knee joint arthroplasty episodes recorded in the National Joint Registry for England and Wales (NJR) and 105,407 episodes from the Norwegian Arthroplasty Register were used to model individual mortality risk over the first year after surgery using flexible parametric survival regression.Aims
Methods
To examine whether natural language processing (NLP) using a clinically based large language model (LLM) could be used to predict patient selection for total hip or total knee arthroplasty (THA/TKA) from routinely available free-text radiology reports. Data pre-processing and analyses were conducted according to the Artificial intelligence to Revolutionize the patient Care pathway in Hip and knEe aRthroplastY (ARCHERY) project protocol. This included use of de-identified Scottish regional clinical data of patients referred for consideration of THA/TKA, held in a secure data environment designed for artificial intelligence (AI) inference. Only preoperative radiology reports were included. NLP algorithms were based on the freely available GatorTron model, a LLM trained on over 82 billion words of de-identified clinical text. Two inference tasks were performed: assessment after model-fine tuning (50 Epochs and three cycles of k-fold cross validation), and external validation.Aims
Methods
A substantial fraction of patients undergoing knee arthroplasty (KA) or hip arthroplasty (HA) do not achieve an improvement as high as the minimal clinically important difference (MCID), i.e. do not achieve a meaningful improvement. Using three patient-reported outcome measures (PROMs), our aim was: 1) to assess machine learning (ML), the simple pre-surgery PROM score, and logistic-regression (LR)-derived performance in their prediction of whether patients undergoing HA or KA achieve an improvement as high or higher than a calculated MCID; and 2) to test whether ML is able to outperform LR or pre-surgery PROM scores in predictive performance. MCIDs were derived using the change difference method in a sample of 1,843 HA and 1,546 KA patients. An artificial neural network, a gradient boosting machine, least absolute shrinkage and selection operator (LASSO) regression, ridge regression, elastic net, random forest, LR, and pre-surgery PROM scores were applied to predict MCID for the following PROMs: EuroQol five-dimension, five-level questionnaire (EQ-5D-5L), EQ visual analogue scale (EQ-VAS), Hip disability and Osteoarthritis Outcome Score-Physical Function Short-form (HOOS-PS), and Knee injury and Osteoarthritis Outcome Score-Physical Function Short-form (KOOS-PS).Aims
Methods
To calculate how the likelihood of obtaining measurable benefit from hip or knee arthroplasty varies with preoperative patient-reported scores. Existing UK data from 222,933 knee and 209,760 hip arthroplasty patients were used to model an individual’s probability of gaining meaningful improvement after surgery based on their preoperative Oxford Knee or Hip Score (OKS/OHS). A clinically meaningful improvement after arthroplasty was defined as ≥ 8 point improvement in OHS, and ≥ 7 in OKS.Aims
Methods
Aims
Patients and Methods
The aim of this study was to define return to
theatre (RTT) rates for elective hip and knee replacement (HR and
KR), to describe the predictors and to show the variations in risk-adjusted
rates by surgical team and hospital using national English hospital
administrative data. We examined information on 260 206 HRs and 315 249 KRs undertaken
between April 2007 and March 2012. The 90-day RTT rates were 2.1%
for HR and 1.8% for KR. Male gender, obesity, diabetes and several
other comorbidities were associated with higher odds for both index
procedures. For HR, hip resurfacing had half the odds of cement fixation
(OR = 0.58, 95% confidence intervals (CI) 0.47 to 0.71). For KR,
unicondylar KR had half the odds of total replacement (OR = 0.49,
95% CI 0.42 to 0.56), and younger ages had higher odds (OR = 2.23,
95% CI 1.65 to 3.01) for ages <
40 years compared with ages 60
to 69 years). There were more funnel plot outliers at three standard deviations
than would be expected if variation occurred on a random basis. Hierarchical modelling showed that three-quarters of the variation
between surgeons for HR and over half the variation between surgeons
for KR are not explained by the hospital they operated at or by
available patient factors. We conclude that 90-day RTT rate may
be a useful quality indicator for orthopaedics. Cite this article:
We reviewed the outcome of 69 uncemented, custom-made,
distal femoral endoprosthetic replacements performed in 69 patients
between 1994 and 2006. There were 31 women and 38 men with a mean
age at implantation of 16.5 years (5 to 37). All procedures were
performed for primary malignant bone tumours of the distal femur.
At a mean follow-up of 124.2 months (4 to 212), 53 patients were
alive, with one patient lost to follow-up. All nine implants (13.0%)
were revised due to aseptic loosening at a mean of 52 months (8
to 91); three implants (4.3%) were revised due to fracture of the
shaft of the prosthesis and three patients (4.3%) had a peri-prosthetic
fracture. Bone remodelling associated with periosteal cortical thinning
adjacent to the uncemented intramedullary stem was seen in 24 patients
but this did not predispose to failure. All aseptically loose implants
in this series were diagnosed to be loose within the first five
years. The results from this study suggest that custom-made uncemented
distal femur replacements have a higher rate of aseptic loosening
compared to published results for this design when used with cemented
fixation. Loosening of uncemented replacements occurs early indicating
that initial fixation of the implant is crucial. Cite this article:
Older patients with multiple medical co-morbidities
are increasingly being offered and undergoing total joint arthroplasty
(TJA). These patients are more likely to require intensive care
support, following surgery. We prospectively evaluated the need
for intensive care admission and intervention in a consecutive series
of 738 patients undergoing elective hip and knee arthroplasty procedures.
The mean age was 60.6 years (18 to 91; 440 women, 298 men. Risk
factors, correlating with the need for critical care intervention,
according to published guidelines, were analysed to identify high-risk
patients who would benefit from post-operative critical care monitoring.
A total of 50 patients (6.7%) in our series required critical care
level interventions during their hospital stay. Six independent
multivariate clinical predictors were identified (p <
0.001)
including a history of congestive heart failure (odds ratio (OR)
24.26, 95% confidence interval (CI) 9.51 to 61.91), estimated blood
loss >
1000 mL (OR 17.36, 95% CI 5.36 to 56.19), chronic obstructive
pulmonary disease (13.90, 95% CI 4.78 to 40.36), intra-operative
use of vasopressors (OR 8.10, 95% CI 3.23 to 20.27), revision hip
arthroplasty (OR 2.71, 95% CI 1.04 to 7.04) and body mass index
>
35 kg/m2 (OR 2.70, 95% CI 123 to 5.94). The model was
then validated against an independent, previously published data
set of 1594 consecutive patients. The use of this risk stratification
model can be helpful in predicting which high-risk patients would
benefit from a higher level of monitoring and care after elective
TJA and aid hospitals in allocating precious critical care resources. Cite this article:
We have developed a novel method of calculating the radiological magnification of the hip using two separate radio-opaque markers. We recruited 74 patients undergoing radiological assessment following total hip replacement. Both the new double marker and a conventional single marker were used by the radiographer at the time of x-ray. The predicted magnification according to each marker was calculated, as was the true radiological magnification of the components. The correlation between true and predicted magnification was good using the double marker (r = 0.90, n = 74, p <
0.001), but only moderate for the single marker (r = 0.50, n = 63, p <
0.001). The median error was significantly less for the double marker than for the single (1.1% The double marker method appears to be superior to the single marker method when used in the clinical environment.